{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#-*- coding:utf-8 -*-\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "inputfile1 = '2_2_2_1greyPredict.xlsx'\n",
    "data = pd.read_excel(inputfile1)\n",
    "data # 1994到2013年间的各个影响因素的数据\n",
    "feature = ['x1', 'x3',  'x5'] # 特征所在列\n",
    "\n",
    "# 准备模型数据\n",
    "data_train = data.loc[range(1999,2014)].copy() # 取2014年前的数据建模\n",
    "data_mean = data_train.mean()\n",
    "data_std = data_train.std()\n",
    "data_train = (data_train-data_mean)/data_std # 均值标准化\n",
    "x_train = data_train[feature].as_matrix() # 特征数据\n",
    "y_train = data_train['y'].as_matrix() # 标签数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n",
      "D:\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=6, input_dim=3)`\n",
      "  if __name__ == '__main__':\n",
      "D:\\Anaconda2\\lib\\site-packages\\keras\\models.py:844: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  warnings.warn('The `nb_epoch` argument in `fit` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2924/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2925/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2926/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2927/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2928/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2929/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2930/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2931/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2932/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2933/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2934/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2935/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2936/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2937/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2938/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2939/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2940/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2941/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2942/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2943/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2944/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2945/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2946/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2947/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2948/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2949/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2950/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2951/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2952/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2953/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2954/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2955/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2956/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2957/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2958/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0092\n",
      "Epoch 2959/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2960/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2961/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2962/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2963/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2964/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2965/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2966/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2967/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2968/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2969/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2970/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2971/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2972/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2973/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2974/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2975/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2976/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2977/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2978/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2979/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2980/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2981/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2982/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2983/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2984/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2985/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2986/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2987/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2988/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2989/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2990/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2991/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2992/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2993/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2994/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2995/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2996/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2997/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2998/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 2999/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "Epoch 3000/3000\n",
      "15/15 [==============================] - 0s - loss: 0.0091\n",
      "训练该模型耗时28.2536235958s!\n"
     ]
    }
   ],
   "source": [
    "# 利用神经网络建模\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation\n",
    "import time \n",
    "start = time.clock()\n",
    "\n",
    "#输入层为３个节点，隐藏层６个节点\n",
    "model = Sequential() # 建立模型\n",
    "model.add(Dense(output_dim =6, input_dim=3)) # 添加输入层、隐藏层节点\n",
    "model.add(Activation('relu')) # 使用relu作为激活函数，可以大幅度提高准确率\n",
    "model.add(Dense(units=1, input_dim=6)) # 添加输出层节点\n",
    "model.compile(loss = 'mean_squared_error', optimizer = 'adam') # 编译模型\n",
    "model.fit(x_train, y_train, nb_epoch = 3000, batch_size=16) #训练模型，学习一千次\n",
    "end = time.clock()\n",
    "usetime = end-start\n",
    "print '训练该模型耗时'+ str(usetime) +'s!'\n",
    "model.save_weights('2_net.model') # 将该模型存储"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QHLN4ERMREakGZcmmoEsXOPbYdLdCRESyRYN8ZF73GEVEpLIaXMDUwsIiIlIV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\nghqIgBkEQRAENRABMwiCIAhq4H+P+OePZYI+jgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8bfbcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 预测并还原结果\n",
    "x = ((data[feature] - data_mean[feature])/data_std[feature]).as_matrix()\n",
    "data[u'y_pred'] = model.predict(x) * data_std['y'] + data_mean['y']\n",
    "#保存的表名命名格式为“2_2_3_1k此表功能名称”，是此小节生成的第1张表格，功能为revenue：增值税预测结果\n",
    "data.to_excel('2_2_3_1zengzhi.xlsx')\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt # 画出预测结果图\n",
    "plt.rc('figure',figsize=(7,7))\n",
    "p = data[['y','y_pred']].plot(subplots = True, style=['b-o', 'r-*'])\n",
    "plt.savefig('zengzhi.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x3</th>\n",
       "      <th>x5</th>\n",
       "      <th>y</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>93.180000</td>\n",
       "      <td>7.980207e+06</td>\n",
       "      <td>0.373051</td>\n",
       "      <td>288972.0</td>\n",
       "      <td>2.654558e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>115.600000</td>\n",
       "      <td>8.779835e+06</td>\n",
       "      <td>0.352216</td>\n",
       "      <td>350495.0</td>\n",
       "      <td>3.923322e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>114.130000</td>\n",
       "      <td>9.554676e+06</td>\n",
       "      <td>0.336237</td>\n",
       "      <td>443213.0</td>\n",
       "      <td>4.389074e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>141.490000</td>\n",
       "      <td>1.050945e+07</td>\n",
       "      <td>0.328014</td>\n",
       "      <td>526377.0</td>\n",
       "      <td>5.193462e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>180.520000</td>\n",
       "      <td>1.314125e+07</td>\n",
       "      <td>0.349630</td>\n",
       "      <td>581898.0</td>\n",
       "      <td>5.495351e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>233.140000</td>\n",
       "      <td>1.594154e+07</td>\n",
       "      <td>0.358193</td>\n",
       "      <td>528365.0</td>\n",
       "      <td>6.182134e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>268.070000</td>\n",
       "      <td>1.843955e+07</td>\n",
       "      <td>0.357756</td>\n",
       "      <td>816119.0</td>\n",
       "      <td>7.468478e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>313.850000</td>\n",
       "      <td>2.227009e+07</td>\n",
       "      <td>0.366172</td>\n",
       "      <td>967265.0</td>\n",
       "      <td>9.576898e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>355.910000</td>\n",
       "      <td>2.602931e+07</td>\n",
       "      <td>0.364540</td>\n",
       "      <td>1115007.0</td>\n",
       "      <td>1.119307e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>389.470000</td>\n",
       "      <td>2.972478e+07</td>\n",
       "      <td>0.358675</td>\n",
       "      <td>1287226.0</td>\n",
       "      <td>1.294621e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>392.820000</td>\n",
       "      <td>3.117342e+07</td>\n",
       "      <td>0.341133</td>\n",
       "      <td>1375085.0</td>\n",
       "      <td>1.396256e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>553.890000</td>\n",
       "      <td>3.644961e+07</td>\n",
       "      <td>0.339120</td>\n",
       "      <td>1594182.0</td>\n",
       "      <td>1.476026e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>596.940000</td>\n",
       "      <td>4.140593e+07</td>\n",
       "      <td>0.333289</td>\n",
       "      <td>1573830.0</td>\n",
       "      <td>1.667290e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>582.520000</td>\n",
       "      <td>4.264156e+07</td>\n",
       "      <td>0.314670</td>\n",
       "      <td>1758311.0</td>\n",
       "      <td>1.819260e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>560.890000</td>\n",
       "      <td>4.754818e+07</td>\n",
       "      <td>0.308351</td>\n",
       "      <td>2216017.0</td>\n",
       "      <td>2.152826e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>767.588538</td>\n",
       "      <td>5.816323e+07</td>\n",
       "      <td>0.329010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.214671e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>862.299420</td>\n",
       "      <td>6.580373e+07</td>\n",
       "      <td>0.327145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.426469e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              x1            x3        x5          y        y_pred\n",
       "1999   93.180000  7.980207e+06  0.373051   288972.0  2.654558e+05\n",
       "2000  115.600000  8.779835e+06  0.352216   350495.0  3.923322e+05\n",
       "2001  114.130000  9.554676e+06  0.336237   443213.0  4.389074e+05\n",
       "2002  141.490000  1.050945e+07  0.328014   526377.0  5.193462e+05\n",
       "2003  180.520000  1.314125e+07  0.349630   581898.0  5.495351e+05\n",
       "2004  233.140000  1.594154e+07  0.358193   528365.0  6.182134e+05\n",
       "2005  268.070000  1.843955e+07  0.357756   816119.0  7.468478e+05\n",
       "2006  313.850000  2.227009e+07  0.366172   967265.0  9.576898e+05\n",
       "2007  355.910000  2.602931e+07  0.364540  1115007.0  1.119307e+06\n",
       "2008  389.470000  2.972478e+07  0.358675  1287226.0  1.294621e+06\n",
       "2009  392.820000  3.117342e+07  0.341133  1375085.0  1.396256e+06\n",
       "2010  553.890000  3.644961e+07  0.339120  1594182.0  1.476026e+06\n",
       "2011  596.940000  4.140593e+07  0.333289  1573830.0  1.667290e+06\n",
       "2012  582.520000  4.264156e+07  0.314670  1758311.0  1.819260e+06\n",
       "2013  560.890000  4.754818e+07  0.308351  2216017.0  2.152826e+06\n",
       "2014  767.588538  5.816323e+07  0.329010        NaN  2.214671e+06\n",
       "2015  862.299420  6.580373e+07  0.327145        NaN  2.426469e+06"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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